Abstract

The implementation of vaccines is one of the most profound advancements in the world of public health. While the scientific process underlying vaccine development is important, determining a vaccine’s efficacy and safety profile would not be possible without vaccine efficacy trials. In terms of trial design, sample size estimation is one of the essential considerations to be made, ensuring that the trial achieves the targeted power, minimizing chances of a false-negative finding. An often considered hypothesis for such trials is testing if a new vaccine is more efficacious than a placebo or the current standard-of-care. We investigated two relevant methods, the Z-Test Normal Approximation and the Exact Conditional Test under Poisson Assumption as presented by Chan and Bohidar. While the Normal Approximation is a generally acceptable method, the Exact Conditional approach may be better suited for trials with low disease incidence. In this Thesis, we develop SAS code for estimating sample size via both methods, in addition to calculating exact power. Sample size and power calculations are performed under a range of different scenarios, stratified by high, intermediate, and low incidence of disease in the control group. Furthermore, under each scenario, simulations are utilized to evaluate the performance of the Exact Conditional approach. Both methods are generally reasonable approaches to sample size estimation and power calculations for vaccine efficacy trials. In trials of higher disease incidence, the Normal Approximation is simpler to implement, and should adequately power the trial. For scenarios of low incidence (< 0.01), the Exact Conditional approach may be more favorable, as it may provide greater power to the trial. To minimize the chance of an underpowered trial, we propose implementing a strategic inflation of sample size. Lastly, we highlight the importance of congruency between methods used for sample size estimation, trial design, and data analysis.Public Health Significance: Vaccine efficacy trials are required for vaccine licensure and distribution to the public. A robustly estimated sample size is crucial, as it ensures a trial is adequately powered, thereby improving the odds of obtaining conclusive results. The methods investigated here represent viable approaches to sample size estimation for such trials.